pm4py.algo.discovery.ocel.ocpn.variants package#

PM4Py – A Process Mining Library for Python

This program is free software: you can redistribute it and/or modify it under the terms of the GNU Affero General Public License as published by the Free Software Foundation, either version 3 of the License, or any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Affero General Public License for more details.

You should have received a copy of the GNU Affero General Public License along with this program. If not, see this software project’s root or visit <https://www.gnu.org/licenses/>.

Website: https://processintelligence.solutions Contact: info@processintelligence.solutions

Submodules#

pm4py.algo.discovery.ocel.ocpn.variants.classic module#

PM4Py – A Process Mining Library for Python

You should have received a copy of the GNU Affero General Public License along with this program. If not, see this software project’s root or visit <https://www.gnu.org/licenses/>.

Website: https://processintelligence.solutions Contact: info@processintelligence.solutions

class pm4py.algo.discovery.ocel.ocpn.variants.classic.Parameters(value, names=<not given>, *values, module=None, qualname=None, type=None, start=1, boundary=None)[source]#

Bases: Enum

EVENT_ACTIVITY = 'param:event:activity'#

OBJECT_TYPE = 'param:object:type'#

INDUCTIVE_MINER_VARIANT = 'inductive_miner_variant'#

DOUBLE_ARC_THRESHOLD = 'double_arc_threshold'#

DIAGNOSTICS_WITH_TBR = 'diagnostics_with_token_based_replay'#

pm4py.algo.discovery.ocel.ocpn.variants.classic.apply(ocel: OCEL, parameters: Dict[Any, Any] | None = None) → Dict[str, Any][source]#

Discovers an object-centric Petri net (without annotation) from the given object-centric event log, using the Inductive Miner as process discovery algorithm.

Reference paper: van der Aalst, Wil MP, and Alessandro Berti. “Discovering object-centric Petri nets.” Fundamenta informaticae 175.1-4 (2020): 1-40.

Parameters#

ocel: Object-centric event log
parameters: Parameters of the algorithm, including: - Parameters.EVENT_ACTIVITY => the activity attribute to be used - Parameters.OBJECT_TYPE => the object type attribute to be used - Parameters.DOUBLE_ARC_THRESHOLD => the threshold for the attribution of the “double arc”, as described in the paper. - Parameters.DIAGNOSTICS_WITH_TBR => performs token-based replay and stores the result in the return dict

Returns#

ocpn

Object-centric Petri net model, presented as a dictionary of properties: - activities: complete set of activities derived from the object-centric event log - object_types: complete set of object types derived from the object-centric event log - edges: dictionary connecting each object type to a set of directly-followed arcs between activities (expressed as tuples,

e.g., (act1, act2)). Every pair of activities is linked to some sets:

event_pairs: the tuples of event identifiers where the directly-follows arc occurs

total_objects: set of tuples containing two event and one object identifier, uniquely identifying an
occurrence of the arc.

activities_indep: dictionary linking each activity, regardless of the object type, to some sets:
- events: the event identifiers where the activity occurs
- unique_objects: the object identifiers where the activity occurs
- total_objects: the tuples of event and object identifiers where the activity occurs.
activities_ot: dictionary linking each object type to another dictionary, where the activities are linked to some sets:
- events: the event identifiers where the activity occurs (with at least one object of the given object type)
- unique_objects: the object identifiers of the given object type where the activity occurs
- total_objects: the tuples of event and object identifiers where the activity occurs.
start_activities: dictionary linking each object type to another dictionary, where the start activities
of the given object type are linked to some sets:
- events: the event identifiers where the start activity occurs (with at least one object of the given object type)
- unique_objects: the object identifiers of the given object type where the start activity occurs
- total_objects: the tuples of event and object identifiers where the start activity occurs.
end_activities: dictionary linking each object type to another dictionary, where the end activities
of the given object type are linked to some sets:
- events: the event identifiers where the end activity occurs (with at least one object of the given object type)
- unique_objects: the object identifiers of the given object type where the end activity occurs
- total_objects: the tuples of event and object identifiers where the end activity occurs.
petri_nets: the accepted Petri nets (Petri net + initial marking + final marking) discovered by the process discovery algorithm
double_arcs_on_activity: dictionary linking each object type to another dictionary, where each arc of the Petri net
is linked to a boolean (True if it is a double arc)
tbr_results: the results of the token-based replay operation (if required)

pm4py.algo.discovery.ocel.ocpn.variants.wo_annotation module#

PM4Py – A Process Mining Library for Python

You should have received a copy of the GNU Affero General Public License along with this program. If not, see this software project’s root or visit <https://www.gnu.org/licenses/>.

Website: https://processintelligence.solutions Contact: info@processintelligence.solutions